YouTube has become a popular social media among the users. Due to YouTube popularity, it became a platform for spammer to distribute spam through the comments on YouTube. This has become a concern because spam can lead to phishing attack which the target can be any user that click any malicious link. Spam has its own features that can be analyzed and detected by classification. Hence, enhancement features are proposed to detect YouTube spam. In order to conduct the experiments, a YouTube Spam detection framework that consists of five (5) phases such as data collection, pre-processing, features selection and extraction, classification and detection were developed. This paper, proposed the YouTube detection framework, examined and validate each of the phases by using two types of data mining tool. The features are constructed from analysis by using data collected from YouTube Spam dataset by using Naïve Bayes and Logistic Regression and tested in two different data mining tools which is Weka and Rapid Miner. From the analysis, thirteen (13) features that had been tested on Weka and RapidMiner shows high accuracy, hence is being used throughout the experiment in this research. Result of Naïve Bayes and Logistic Regression run in Weka is slightly higher than RapidMiner. In addition, result of Naïve Bayes is higher than Logistic Regression with 87.21% and 85.29% respectively in Weka. While in RapidMiner there is slightly different of accuracy between Naïve Bayes and Logistic Regression 80.41% and 80.88%. But, precision of Naïve Bayes is higher than Logistic Regression.

This study aims to investigate the determinants of the privacy protection behaviour strategies that been employed by users while utilising SNSs. By understanding the determinants of privacy protection will be able to generate awareness that can protect users and allow them to confidently impose their self-control through the execution of privacy protection behaviour strategies. The finding has shown that there was a significant relationship of perceived severity, perceived vulnerability, response efficacy and self-efficacy towards information privacy concern as well as a significant relationship of information privacy concern and privacy protection behaviour strategies. This research is crucial as it serves as a guide that provides instructions and guidelines that help users of SNSs to keep their privacy intact.

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The telecommunication industry is the leading industry in big data trends as this industry has the most capable infrastructure for big data. However, the adoption of big data in telecommunication services also raises important security and privacy challenges due to the high volume, velocity, and variety of big data characteristics. To address the issue, this study shed light on the security and privacy challenges of big data adoption in the telecommunication industry. This study focuses on investigating the security and privacy challenges for data users in telecommunication services from the lens of the TOE framework and examines the mitigation strategies to address the privacy and security challenges. This study is conducted using qualitative methodology. From the perspectives of data users (telecommunication providers), it could be concluded that data management, data privacy, data compliance, and regulatory orchestration challenges are the most pressing concerns in big data adoption. This study offers contributions in presenting a thematic classification of security and privacy challenges and their mitigation strategies for big data adoption in the telecommunication industry. The thematic classification highlights potential gaps for future research in the big data security domain. This study is significant in that it provides empirical evidence for the perspectives of telecommunication data users in addressing privacy and security issues that are related to big data adoption.

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